背景

2015年台灣經歷近十年來最嚴重的登革熱大流行，造成43784人確診及5.15%死亡率，其中台南確診22777人。登革出血熱接受適當的治療後死亡率可降至0.2%以下，因此病人在登革熱的哪個時期確診對於其是否會進展至嚴重登革熱甚至死亡是非常重要的，早期偵測這些造成疾病惡化的因子並加以處理是必須的，此研究的目的是去識別病人發燒的特徵及臨床的表現是否可預測疾病的嚴重程度，是否會造成之後嚴重的血小板低下甚至死亡。

方法

此研究納入289個確診登革熱的住院病人，其中22人死亡及267人存活。首先，我們追蹤病人十二天並紀錄其血小板的變化，然後利用群組化軌跡模式把血小板數值隨著時間變化的情形分成二天類，藉此研究血小板低下的嚴重程度與在急診臨床及實驗室數據之間的關係，接著，我們比較有無死亡兩組病人的急診臨床及實驗室數據，再利用多變數邏輯斯迴歸分析找出有意義的風險因子，然後找出其ROC 曲線，最後利用諾模圖(nomogram)來建立一個風險計分系統，期望之後可利用在臨床.

結果

在289人裡，162人被分在中度及嚴重血小板低下的軌跡，另外127人被分在輕度血小板的軌跡。中度及嚴重血小板低下的病人年紀較高，有較高比例有冠心病病史也有較高比例在急診有接受呼吸器治療，經過多變數邏輯斯迴歸分析並調整年齡，有無呼吸器治療及有無冠心病病史這些因子後，年紀超過85歲的病人比起小於85歲的病人的勝算比是8.62，急診有無接受呼吸器治療的勝算比是11，有無冠心病病史的勝算比是2.34.，中度及嚴重血小板低下的病人其ROC曲線底下的面積是0.75。
死亡組的病人有較高的比例年紀較高且有多的慢性疾病，像慢性腎病及冠心病， 經過多變數邏輯斯迴歸分析，年紀超過85歲的病人比起小於85歲的病人的勝算比是29.2，發燒離到急診時間小於4天的勝算比是4.71，在急診有無低血壓狀況的勝算比是7.81，有無心衰竭病史的勝算比是11.85，死亡的病人其ROC曲線底下的面積是0.88，以上這四個因子被做成諾模圖看是否可應用在臨床。

結論

根據研究的結果，病人的年紀、發燒的狀況、在急診血壓的變化、血小板的數量及有無心衰竭的病史皆和病人的預後息息相關，綜合以上的臨床發現也許可以幫助醫師預測病人之後疾病的嚴重程度。

Background

In 2015, Taiwan experienced the worst dengue virus (DENV) outbreak in 10 years, with a high DF death rate (5.15%) and 43,784 notified cases, of which 22,777 (52%) occurred in Tainan. With careful treatment, the fatality rate of DSS can be reduced to<0.2%. Therefore, the stage of the disease during which a patient is diagnosed is critical in determining the accuracy with which physicians can predict the possibility of progression to SD and death. Early identification of risk factors associated with disease progression from mild to severe dengue and death is essential in determining subsequent management. The purpose of this study was to identify febrile characteristics and clinical presentations that can be used as predictors of thrombocytopenia and fatality in hospitalized adult patients with DENV infections.

Methods

This study examined 289 adult hospitalized patients with laboratory-confirmed DENV infections, of which 22 were fatal and 267 were non-fatal. First, we observed each patient for 12 days and computed the daily platelet counts. The daily platelet counts were incorporated into a group-based trajectory model (GBTM) to provide distinct patterns of thrombocytopenia. Using group-based trajectory modeling, the patients were categorized into groups based on their severity of thrombocytopenia. Then, we examine the relationship between thrombocytopenia and clinical and laboratory characteristics at the time of ED admission. Second. A comparison of the clinical and laboratory characteristics was retrospectively conducted of the deceased and surviving individuals. Next, multivariable logistic regression analysis and receiver operating characteristic (ROC) curve were performed to identify potential predictors of fatality and thrombocytopenia. Finally, the final logistic regression model was converted to a nomogram for ease of use in clinical practice.

Results

Among 289 patients, 162 patients were classified into moderate to severe thrombocytopenia trajectory and 127 patients were mild thrombocytopenia trajectory. Compared with patients with mild thrombocytopenia trajectory, patients with moderate to severe thrombocytopenia trajectory have higher risk to require ventilator support during ED admission and have more ischemic heart disease. In the multivariable logistic analysis, age>85 vs. age<55 [odds ratio (OR), 8.62; 95% confidence interval (CI), 2.12–35.0], mechanical ventilation (OR, 11.0; 95% CI, 1.42–85.2), and comorbidity with ischemic heart disease (OR, 2.34; 95% CI, 1.05–5.23) were independent predictive factors for moderate to severe thrombocytopenia after adjusting for age, mechanical ventilation and comorbidity with ischemic disease. The ROC curve analysis indicated that the final prognostic model yielded an area under the curve (AUC) of 0.75 (95% CI, 0.69–0.81) for moderate to severe thrombocytopenia.
Fatal patients exhibited significantly more medical comorbidities, particularly renal and cardiac disorders, and they were, in general, older than control individuals (p<0.0001). The results of multivariable logistic regression analysis showed that age>85 vs. age<55 (odds ratio (OR), 29.2; 95% confidence interval (CI), 1.30–653.3), febrile duration of less than four days before arriving in the emergency department (ED) (odds ratio (OR), 4.71; 95% confidence interval (CI), 1.24–17.92), episode of hypotension in the ED (OR, 7.81; 95% CI, 2.56–23.86) and comorbidity with congestive heart failure (OR, 11.85; 95% CI, 2.44–57.55) were all significantly associated with inpatient fatality due to DENV infection. The ROC curve analysis indicated that the final prognostic model yielded an area under the curve (AUC) of 0.88 for fatality. Nomogram was developed to predict fatality due to dengue virus infection using the 5 independent parameters. The nomogram is used by totaling the points assigned on the scales for each independent parameter.

Conclusion

According to our results, age, febrile pattern, episode of hypotension in the ED, platelet count and comorbidity with congestive heart failure are related to patient’s outcome. The aforementioned clinical findings may help clinicians predict fatality among adult inpatients with DENV infection.

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